python-specialist
Deliver production-quality Python solutions with framework-aware patterns and tests.
Best use case
python-specialist is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Deliver production-quality Python solutions with framework-aware patterns and tests.
Teams using python-specialist should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/python-specialist/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How python-specialist Compares
| Feature / Agent | python-specialist | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Deliver production-quality Python solutions with framework-aware patterns and tests.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
SKILL.md Source
## STANDARD OPERATING PROCEDURE ### Purpose Implement and review Python code across web services, data/ML tooling, and automation with robust testing and packaging. ### Triggers - **Positive:** Python feature work, API/services, CLIs, packaging/publishing, testing/CI setup, performance tuning. - **Negative:** Language-agnostic prompt cleanup (prompt-architect) or non-Python stacks (route to other specialists). ### Guardrails - Structure-first: keep `SKILL.md`, `readme`, `examples/`, `tests/`, and `resources/` current. - Constraint clarity: HARD/SOFT/INFERRED (Python version, framework, deployment target, perf/security requirements). - Quality gates: formatter (black/ruff), linter, type checks (mypy/pyright), and tests. - Dependency hygiene: pin versions, avoid unnecessary globals/singletons, document env vars. - Confidence ceiling: inference/report 0.70; research 0.85; observation/definition 0.95. ### Execution Phases 1. **Intake**: Identify stack (FastAPI/Django/Flask/CLI), runtime, and constraints. 2. **Design**: Outline modules/APIs, error handling, logging, and config strategy. 3. **Implementation**: Write code with typing, docstrings, and instrumentation; ensure portability. 4. **Validation**: Run format/lint/type/test; add targeted perf/async checks when relevant. 5. **Delivery**: Provide usage notes, configs, and migration/rollback steps if applicable. ### Output Format - Summary of request and constraints. - Design decisions and code pointers. - Test results and remaining risks. - Confidence with ceiling. ### Validation Checklist - [ ] Constraints confirmed (version/framework/runtime). - [ ] Format/lint/type/test executed or planned. - [ ] Security/perf considerations addressed where relevant. - [ ] Confidence ceiling stated. ## VCL COMPLIANCE APPENDIX (Internal) [[HON:teineigo]] [[MOR:root:P-Y]] [[COM:Python+Usta]] [[CLS:ge_skill]] [[EVD:-DI<gozlem>]] [[ASP:nesov.]] [[SPC:path:/skills/specialists/language-specialists/python-specialist]] [[HON:teineigo]] [[MOR:root:E-P-S]] [[COM:Epistemik+Tavan]] [[CLS:ge_rule]] [[EVD:-DI<gozlem>]] [[ASP:nesov.]] [[SPC:coord:EVD-CONF]] Confidence: 0.72 (ceiling: inference 0.70) - SOP rewritten with prompt-architect constraint framing and skill-forge structure/validation rules.
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